Robotic-assisted rehabilitation has become an essential field in supporting the functional recovery of patients with neurological, musculoskeletal or post-traumatic conditions. This paper provides a systematic and applicative analysis of the control algorithms used in robotic rehabilitation systems, with a focus on the functional classification: position control, force, impedance, adaptive, artificial intelligence-based and hybrid schemes. The characteristics of each type of control, clinical applications, advantages and technical limitations are discussed in detail, illustrated by block diagrams and comparative graphs. The paper also includes a synthesis of existing commercial systems, a multi-criteria evaluation of the performance of the algorithms and an analysis of emerging trends in the recent literature (2020–2024). Current challenges regarding sensor integration, system standardization, real-time clinical feasibility and the applicability of brain–machine interfaces or adaptive myoelectric prostheses are discussed. The results obtained can support the development of efficient, safe and personalized solutions in the field of robotic rehabilitation.
Rad et al. (Thu,) studied this question.